Jurnal ULTIMA Computing
Vol 11 No 2 (2019): Ultima Computing : Jurnal Sistem Komputer

Implementasi Jaringan Saraf Tiruan Backpropagation untuk Pengenalan Karakter pada Dokumen Tercetak

Alethea Suryadibrata (Unknown)
Dian Puspita Chandra (Unknown)



Article Info

Publish Date
16 Jan 2020

Abstract

Digital documents from the scanner device cannot be edited. To be able to edit digital documents, Optical Character Recognition (OCR) technology is needed. This research was conducted with the aim of implementing backpropagation artificial neural networks in printed documents and to find out how the accuracy of the implementation of backpropagation artificial neural networks in printed documents. This research uses multilayer networks with three layers. The input layer consists of 225 nodes with 15 × 15 pixels digital image as input, hidden layer consists of 110 nodes, and the output layer consists of 54 nodes representing A-Z, a-z, point punctuation (.), and comma punctuation (,). The learning rate used in this research is 0,29. The average accuracy level obtained from the implementation of backpropagation artificial neural networks in this research was 94 % for Ms Arial Unicode font type, 96,6 % for Tahoma font type, and 94 % for Times New Roman font type.

Copyrights © 2019






Journal Info

Abbrev

SK

Publisher

Subject

Computer Science & IT Control & Systems Engineering Engineering

Description

urnal ULTIMA Computing merupakan Jurnal Program Studi Sistem Komputer Universitas Multimedia Nusantara yang menyajikan artikel-artikel penelitian ilmiah dalam bidang Sistem Komputer serta isu-isu teoritis dan praktis yang terkini, mencakup komputasi, organisasi dan arsitektur komputer, programming, ...